In pathology or cytology, it is often desirable to locate and measure cells or nuclei using an automated or semi-automated instrument. Such instruments may be used for research, or for screening. An example of the latter is the screening for cervical cancer using the Papanicolou test (or Pap test). These instruments acquire and analyze digital images to locate cells of interest or to classify slides as being normal or suspect.
In the analysis of objects in digital images it is essential that the objects be distinguished from the background of the image. To characterize cells or objects, the objects must first be located. The process of locating objects within the digital image is known as “segmentation.” A variety of techniques are used in the segmentation process to locate the objects of interest so that subsequent computer analysis can characterize the objects. For example, segmentation of an image containing cells might allow the cell's nucleus and/or cytoplasm to be located.
A traditional approach to the task of locating and classifying objects within an image involves several stages: first—segmenting the image to create a binary mask of the objects; then—labeling the objects in this mask, with each connected set of pixels assigned a different label; and finally—measuring various features of the labeled objects.
One of the techniques used for segmenting images is “thresholding.” In this technique, a threshold value of image brightness is chosen and each pixel in the image is then compared with this threshold value. Pixels with a brightness value above this threshold are considered background pixels; pixels with values below the threshold are considered object pixels. The threshold value for locating objects may be chosen based on an image histogram, which is a frequency distribution of the darkness values found within an image. A thresholding algorithm may find a single threshold value using these histograms. For instance, the threshold value might be half-way between the darkest and lightest pixels. Alternatively, the threshold value might be chosen as an inflection point between the abundant “background” pixels and the more rare “object” pixels. Finding an ideal threshold for each object in an image is a difficult task. Often a single threshold value is not optimal for multiple objects with varying darkness values within an entire image.
Once the threshold value is chosen and the thresholding process is completed, the “object” pixels can form a binary mask of the objects in the image. A boundary around the mask might be used to represent each object. The boundary might or might not reflect the object accurately. Many methods have been developed to refine the boundary once it is located. Such methods may use darkness information near the boundary, or constraints such as gradient, curvature, “closeness to a circle,” etc. to refine boundaries.
Currently known techniques for image segmentation are often complex and time consuming. These techniques do not always yield high accuracy in the segmentation process, particularly if there is little contrast between the object to be located and the background surrounding it. Consequently, current segmentation algorithms often fail to locate objects properly. In cell image analysis, for example, a cell nucleus might be incorrectly segmented because the located boundary is too large or too small. This can result in false positive events (the instrument incorrectly calls a normal object suspicious) or false negative events (the instrument misses a true suspicious object).
There is a need for improved segmentation for automated imaging and automated imaging devices, in particular for the accurate identification of object boundaries.
Whatever the precise merits, features, and advantages of currently known segmentation techniques, none of them achieve or fulfill the purposes of the present invention.